{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"float:left; border:none\">\n",
    "   <tr style=\"border:none; background-color: #ffffff\">\n",
    "       <td style=\"border:none\">\n",
    "           <a href=\"http://bokeh.org/\">     \n",
    "           <img \n",
    "               src=\"assets/bokeh-transparent.png\" \n",
    "               style=\"width:50px\"\n",
    "           >\n",
    "           </a>    \n",
    "       </td>\n",
    "       <td style=\"border:none\">\n",
    "           <h1>Bokeh Tutorial</h1>\n",
    "       </td>\n",
    "   </tr>\n",
    "</table>\n",
    "\n",
    "<div style=\"float:right;\"><h2>03. Data Sources and Transformations</h2></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.io import output_notebook, show\n",
    "from bokeh.plotting import figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1001\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };var element = document.getElementById(\"1001\");\n",
       "  if (element == null) {\n",
       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"1001\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview\n",
    "\n",
    "We've seen how Bokeh can work well with Python lists, NumPy arrays, Pandas series, etc. At lower levels, these inputs are converted to a Bokeh `ColumnDataSource`. This data type is the central data source object used throughout Bokeh. Although Bokeh often creates them for us transparently, there are times when it is useful to create them explicitly.\n",
    "\n",
    "In later sections we will see features like hover tooltips, computed transforms, and CustomJS interactions that make use of the `ColumnDataSource`, so let's take a quick look now. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating with Python Dicts\n",
    "\n",
    "The `ColumnDataSource` can be imported from `bokeh.models`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.models import ColumnDataSource"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `ColumnDataSource` is a mapping of column names (strings) to sequences of values. Here is a simple example. The mapping is provided by passing a Python `dict` with string keys and simple Python lists as values. The values could also be NumPy arrays, or Pandas sequences.\n",
    "\n",
    "***NOTE: ALL the columns in a `ColumnDataSource` must always be the SAME length.***\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "source = ColumnDataSource(data={\n",
    "    'x' : [1, 2, 3, 4, 5],\n",
    "    'y' : [3, 7, 8, 5, 1],\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Up until now we have called functions like `p.circle` by passing in literal lists or arrays of data directly, when we do this, Bokeh creates a `ColumnDataSource` for us, automatically. But it is possible to specify a `ColumnDataSource` explicitly by passing it as the `source` argument to a glyph method. Whenever we do this, if we want a property (like `\"x\"` or `\"y\"` or `\"fill_color\"`) to have a sequence of values, we pass the ***name of the column*** that we would like to use for a property:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"ffcadcc6-dc0e-4b02-a5c7-48b68d47b7da\" data-root-id=\"1003\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"764830bf-0e4d-478b-a836-644593b03cf5\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1012\",\"type\":\"LinearAxis\"}],\"center\":[{\"id\":\"1016\",\"type\":\"Grid\"},{\"id\":\"1021\",\"type\":\"Grid\"}],\"left\":[{\"id\":\"1017\",\"type\":\"LinearAxis\"}],\"plot_height\":400,\"plot_width\":400,\"renderers\":[{\"id\":\"1038\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"1041\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"1028\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"1004\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"1008\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"1006\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"1010\",\"type\":\"LinearScale\"}},\"id\":\"1003\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{},\"id\":\"1027\",\"type\":\"HelpTool\"},{\"attributes\":{},\"id\":\"1010\",\"type\":\"LinearScale\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":[1,2,3,4,5],\"y\":[3,7,8,5,1]},\"selected\":{\"id\":\"1047\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1048\",\"type\":\"UnionRenderers\"}},\"id\":\"1002\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1022\",\"type\":\"PanTool\"},{\"id\":\"1023\",\"type\":\"WheelZoomTool\"},{\"id\":\"1024\",\"type\":\"BoxZoomTool\"},{\"id\":\"1025\",\"type\":\"SaveTool\"},{\"id\":\"1026\",\"type\":\"ResetTool\"},{\"id\":\"1027\",\"type\":\"HelpTool\"}]},\"id\":\"1028\",\"type\":\"Toolbar\"},{\"attributes\":{\"formatter\":{\"id\":\"1043\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1013\",\"type\":\"BasicTicker\"}},\"id\":\"1012\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1018\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1013\",\"type\":\"BasicTicker\"},{\"attributes\":{\"ticker\":{\"id\":\"1013\",\"type\":\"BasicTicker\"}},\"id\":\"1016\",\"type\":\"Grid\"},{\"attributes\":{\"fill_color\":{\"value\":\"#1f77b4\"},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":20},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1036\",\"type\":\"Circle\"},{\"attributes\":{\"data_source\":{\"id\":\"1002\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1036\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1037\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"1039\",\"type\":\"CDSView\"}},\"id\":\"1038\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1047\",\"type\":\"Selection\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"1018\",\"type\":\"BasicTicker\"}},\"id\":\"1021\",\"type\":\"Grid\"},{\"attributes\":{\"source\":{\"id\":\"1002\",\"type\":\"ColumnDataSource\"}},\"id\":\"1039\",\"type\":\"CDSView\"},{\"attributes\":{\"formatter\":{\"id\":\"1045\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1018\",\"type\":\"BasicTicker\"}},\"id\":\"1017\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1043\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1045\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1048\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1046\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"text\":\"\"},\"id\":\"1041\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1022\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"1023\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"1026\",\"type\":\"ResetTool\"},{\"attributes\":{\"callback\":null},\"id\":\"1004\",\"type\":\"DataRange1d\"},{\"attributes\":{\"overlay\":{\"id\":\"1046\",\"type\":\"BoxAnnotation\"}},\"id\":\"1024\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":20},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1037\",\"type\":\"Circle\"},{\"attributes\":{\"callback\":null},\"id\":\"1006\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1025\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"1008\",\"type\":\"LinearScale\"}],\"root_ids\":[\"1003\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"764830bf-0e4d-478b-a836-644593b03cf5\",\"roots\":{\"1003\":\"ffcadcc6-dc0e-4b02-a5c7-48b68d47b7da\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1003"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = figure(plot_width=400, plot_height=400)\n",
    "p.circle('x', 'y', size=20, source=source)\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exercise: create a column data source with NumPy arrays as column values and plot it\n",
    "\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating with Pandas DataFrames\n",
    "\n",
    "It's also simple to create `ColumnDataSource` objects directly from Pandas data frames. To do this, just pass the data frame to  `ColumnDataSource` when you create it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.sampledata.iris import flowers as df\n",
    "\n",
    "source = ColumnDataSource(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use it as we did above by passing the column names to glyph methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"4088619d-2f07-435e-84fc-fa4e1d6491e8\" data-root-id=\"1104\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"1ccc1aa7-38db-416c-904e-09aa98c6126a\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1113\",\"type\":\"LinearAxis\"}],\"center\":[{\"id\":\"1117\",\"type\":\"Grid\"},{\"id\":\"1122\",\"type\":\"Grid\"}],\"left\":[{\"id\":\"1118\",\"type\":\"LinearAxis\"}],\"plot_height\":400,\"plot_width\":400,\"renderers\":[{\"id\":\"1139\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"1151\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"1129\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"1105\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"1109\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"1107\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"1111\",\"type\":\"LinearScale\"}},\"id\":\"1104\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"callback\":null},\"id\":\"1105\",\"type\":\"DataRange1d\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"1119\",\"type\":\"BasicTicker\"}},\"id\":\"1122\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1114\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1127\",\"type\":\"ResetTool\"},{\"attributes\":{\"data_source\":{\"id\":\"1103\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1137\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1138\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"1140\",\"type\":\"CDSView\"}},\"id\":\"1139\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1128\",\"type\":\"HelpTool\"},{\"attributes\":{},\"id\":\"1153\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1155\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1111\",\"type\":\"LinearScale\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"petal_length\"},\"y\":{\"field\":\"petal_width\"}},\"id\":\"1138\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"1109\",\"type\":\"LinearScale\"},{\"attributes\":{\"text\":\"\"},\"id\":\"1151\",\"type\":\"Title\"},{\"attributes\":{\"ticker\":{\"id\":\"1114\",\"type\":\"BasicTicker\"}},\"id\":\"1117\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1158\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"source\":{\"id\":\"1103\",\"type\":\"ColumnDataSource\"}},\"id\":\"1140\",\"type\":\"CDSView\"},{\"attributes\":{\"formatter\":{\"id\":\"1155\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1119\",\"type\":\"BasicTicker\"}},\"id\":\"1118\",\"type\":\"LinearAxis\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1123\",\"type\":\"PanTool\"},{\"id\":\"1124\",\"type\":\"WheelZoomTool\"},{\"id\":\"1125\",\"type\":\"BoxZoomTool\"},{\"id\":\"1126\",\"type\":\"SaveTool\"},{\"id\":\"1127\",\"type\":\"ResetTool\"},{\"id\":\"1128\",\"type\":\"HelpTool\"}]},\"id\":\"1129\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"1157\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1123\",\"type\":\"PanTool\"},{\"attributes\":{\"callback\":null},\"id\":\"1107\",\"type\":\"DataRange1d\"},{\"attributes\":{\"callback\":null,\"data\":{\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149],\"petal_length\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[150]},\"petal_width\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[150]},\"sepal_length\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[150]},\"sepal_width\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[150]},\"species\":[\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\"]},\"selected\":{\"id\":\"1157\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1158\",\"type\":\"UnionRenderers\"}},\"id\":\"1103\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"overlay\":{\"id\":\"1156\",\"type\":\"BoxAnnotation\"}},\"id\":\"1125\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"1124\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1156\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"formatter\":{\"id\":\"1153\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1114\",\"type\":\"BasicTicker\"}},\"id\":\"1113\",\"type\":\"LinearAxis\"},{\"attributes\":{\"fill_color\":{\"value\":\"#1f77b4\"},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"petal_length\"},\"y\":{\"field\":\"petal_width\"}},\"id\":\"1137\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"1126\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"1119\",\"type\":\"BasicTicker\"}],\"root_ids\":[\"1104\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"1ccc1aa7-38db-416c-904e-09aa98c6126a\",\"roots\":{\"1104\":\"4088619d-2f07-435e-84fc-fa4e1d6491e8\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1104"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = figure(plot_width=400, plot_height=400)\n",
    "p.circle('petal_length', 'petal_width', source=source)\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exercise: create a column data source with the autompg sample data frame and plot it\n",
    "\n",
    "from bokeh.sampledata.autompg import autompg_clean as df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automatic Conversion\n",
    "\n",
    "If you do not need to share data sources, it may be convenient to pass dicts, Pandas `DataFrame` or `GroupBy` objects directly to glhyph methods, without explicitly creating a `ColumnDataSource`. In this case, a `ColumnDataSource` will be created automatically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"17481e12-6f84-4b58-865e-c8a8b252edd7\" data-root-id=\"1213\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"e552e890-2455-49b5-a5ae-dffaf245cee7\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1222\",\"type\":\"LinearAxis\"}],\"center\":[{\"id\":\"1226\",\"type\":\"Grid\"},{\"id\":\"1231\",\"type\":\"Grid\"}],\"left\":[{\"id\":\"1227\",\"type\":\"LinearAxis\"}],\"plot_height\":400,\"plot_width\":400,\"renderers\":[{\"id\":\"1249\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"1270\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"1238\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"1214\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"1218\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"1216\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"1220\",\"type\":\"LinearScale\"}},\"id\":\"1213\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1232\",\"type\":\"PanTool\"},{\"id\":\"1233\",\"type\":\"WheelZoomTool\"},{\"id\":\"1234\",\"type\":\"BoxZoomTool\"},{\"id\":\"1235\",\"type\":\"SaveTool\"},{\"id\":\"1236\",\"type\":\"ResetTool\"},{\"id\":\"1237\",\"type\":\"HelpTool\"}]},\"id\":\"1238\",\"type\":\"Toolbar\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"petal_length\"},\"y\":{\"field\":\"petal_width\"}},\"id\":\"1248\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"1276\",\"type\":\"Selection\"},{\"attributes\":{\"callback\":null},\"id\":\"1216\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1277\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"text\":\"\"},\"id\":\"1270\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1232\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"1218\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1233\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"overlay\":{\"id\":\"1275\",\"type\":\"BoxAnnotation\"}},\"id\":\"1234\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"data_source\":{\"id\":\"1245\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1247\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1248\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"1250\",\"type\":\"CDSView\"}},\"id\":\"1249\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_color\":{\"value\":\"#1f77b4\"},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"petal_length\"},\"y\":{\"field\":\"petal_width\"}},\"id\":\"1247\",\"type\":\"Circle\"},{\"attributes\":{\"formatter\":{\"id\":\"1274\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1228\",\"type\":\"BasicTicker\"}},\"id\":\"1227\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1235\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"1236\",\"type\":\"ResetTool\"},{\"attributes\":{\"callback\":null},\"id\":\"1214\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1274\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1275\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"formatter\":{\"id\":\"1272\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1223\",\"type\":\"BasicTicker\"}},\"id\":\"1222\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1237\",\"type\":\"HelpTool\"},{\"attributes\":{\"source\":{\"id\":\"1245\",\"type\":\"ColumnDataSource\"}},\"id\":\"1250\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1228\",\"type\":\"BasicTicker\"},{\"attributes\":{\"callback\":null,\"data\":{\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149],\"petal_length\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[150]},\"petal_width\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[150]},\"sepal_length\":{\"__ndarray__\":\"ZmZmZmZmFECamZmZmZkTQM3MzMzMzBJAZmZmZmZmEkAAAAAAAAAUQJqZmZmZmRVAZmZmZmZmEkAAAAAAAAAUQJqZmZmZmRFAmpmZmZmZE0CamZmZmZkVQDMzMzMzMxNAMzMzMzMzE0AzMzMzMzMRQDMzMzMzMxdAzczMzMzMFkCamZmZmZkVQGZmZmZmZhRAzczMzMzMFkBmZmZmZmYUQJqZmZmZmRVAZmZmZmZmFEBmZmZmZmYSQGZmZmZmZhRAMzMzMzMzE0AAAAAAAAAUQAAAAAAAABRAzczMzMzMFEDNzMzMzMwUQM3MzMzMzBJAMzMzMzMzE0CamZmZmZkVQM3MzMzMzBRAAAAAAAAAFkCamZmZmZkTQAAAAAAAABRAAAAAAAAAFkCamZmZmZkTQJqZmZmZmRFAZmZmZmZmFEAAAAAAAAAUQAAAAAAAABJAmpmZmZmZEUAAAAAAAAAUQGZmZmZmZhRAMzMzMzMzE0BmZmZmZmYUQGZmZmZmZhJAMzMzMzMzFUAAAAAAAAAUQAAAAAAAABxAmpmZmZmZGUCamZmZmZkbQAAAAAAAABZAAAAAAAAAGkDNzMzMzMwWQDMzMzMzMxlAmpmZmZmZE0BmZmZmZmYaQM3MzMzMzBRAAAAAAAAAFECamZmZmZkXQAAAAAAAABhAZmZmZmZmGEBmZmZmZmYWQM3MzMzMzBpAZmZmZmZmFkAzMzMzMzMXQM3MzMzMzBhAZmZmZmZmFkCamZmZmZkXQGZmZmZmZhhAMzMzMzMzGUBmZmZmZmYYQJqZmZmZmRlAZmZmZmZmGkAzMzMzMzMbQM3MzMzMzBpAAAAAAAAAGEDNzMzMzMwWQAAAAAAAABZAAAAAAAAAFkAzMzMzMzMXQAAAAAAAABhAmpmZmZmZFUAAAAAAAAAYQM3MzMzMzBpAMzMzMzMzGUBmZmZmZmYWQAAAAAAAABZAAAAAAAAAFkBmZmZmZmYYQDMzMzMzMxdAAAAAAAAAFEBmZmZmZmYWQM3MzMzMzBZAzczMzMzMFkDNzMzMzMwYQGZmZmZmZhRAzczMzMzMFkAzMzMzMzMZQDMzMzMzMxdAZmZmZmZmHEAzMzMzMzMZQAAAAAAAABpAZmZmZmZmHkCamZmZmZkTQDMzMzMzMx1AzczMzMzMGkDNzMzMzMwcQAAAAAAAABpAmpmZmZmZGUAzMzMzMzMbQM3MzMzMzBZAMzMzMzMzF0CamZmZmZkZQAAAAAAAABpAzczMzMzMHkDNzMzMzMweQAAAAAAAABhAmpmZmZmZG0BmZmZmZmYWQM3MzMzMzB5AMzMzMzMzGUDNzMzMzMwaQM3MzMzMzBxAzczMzMzMGEBmZmZmZmYYQJqZmZmZmRlAzczMzMzMHECamZmZmZkdQJqZmZmZmR9AmpmZmZmZGUAzMzMzMzMZQGZmZmZmZhhAzczMzMzMHkAzMzMzMzMZQJqZmZmZmRlAAAAAAAAAGECamZmZmZkbQM3MzMzMzBpAmpmZmZmZG0AzMzMzMzMXQDMzMzMzMxtAzczMzMzMGkDNzMzMzMwaQDMzMzMzMxlAAAAAAAAAGkDNzMzMzMwYQJqZmZmZmRdA\",\"dtype\":\"float64\",\"shape\":[150]},\"sepal_width\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[150]},\"species\":[\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"setosa\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"versicolor\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\",\"virginica\"]},\"selected\":{\"id\":\"1276\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1277\",\"type\":\"UnionRenderers\"}},\"id\":\"1245\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"ticker\":{\"id\":\"1223\",\"type\":\"BasicTicker\"}},\"id\":\"1226\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1272\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1220\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1223\",\"type\":\"BasicTicker\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"1228\",\"type\":\"BasicTicker\"}},\"id\":\"1231\",\"type\":\"Grid\"}],\"root_ids\":[\"1213\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"e552e890-2455-49b5-a5ae-dffaf245cee7\",\"roots\":{\"1213\":\"17481e12-6f84-4b58-865e-c8a8b252edd7\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1213"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.sampledata.iris import flowers as df\n",
    "\n",
    "p = figure(plot_width=400, plot_height=400)\n",
    "p.circle('petal_length', 'petal_width', source=df)\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformations\n",
    "\n",
    "In addition to being configured with names of columns from data sources, glyph properties may also be configured with transform objects that represent transformations of columns. These live in the `bokeh.transform` module. It is important to note that when doing using these objects, the tranformations occur *in the browser, not in Python*. \n",
    "\n",
    "The first transform we look at is the `cumsum` transform, which can generate a new sequence of values from a data source column by cumulatively summing the values in the column. This can be usefull for pie or donut type charts as seen below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"197247e6-bc2a-4b33-9163-2b0c00a2bca4\" data-root-id=\"1447\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"33922f4a-611d-4279-8c64-4e9672655774\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1458\",\"type\":\"LinearAxis\"}],\"center\":[{\"id\":\"1462\",\"type\":\"Grid\"},{\"id\":\"1467\",\"type\":\"Grid\"},{\"id\":\"1484\",\"type\":\"Legend\"}],\"left\":[{\"id\":\"1463\",\"type\":\"LinearAxis\"}],\"plot_height\":350,\"renderers\":[{\"id\":\"1477\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"1448\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"1469\",\"type\":\"Toolbar\"},\"toolbar_location\":null,\"x_range\":{\"id\":\"1450\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"1454\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"1452\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"1456\",\"type\":\"LinearScale\"}},\"id\":\"1447\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{},\"id\":\"1459\",\"type\":\"BasicTicker\"},{\"attributes\":{\"end_angle\":{\"expr\":{\"id\":\"1472\",\"type\":\"CumSum\"},\"units\":\"rad\"},\"fill_color\":{\"field\":\"color\"},\"line_color\":{\"value\":\"white\"},\"radius\":{\"units\":\"data\",\"value\":0.4},\"start_angle\":{\"expr\":{\"id\":\"1471\",\"type\":\"CumSum\"},\"units\":\"rad\"},\"x\":{\"value\":0},\"y\":{\"value\":1}},\"id\":\"1475\",\"type\":\"Wedge\"},{\"attributes\":{\"source\":{\"id\":\"1473\",\"type\":\"ColumnDataSource\"}},\"id\":\"1478\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1464\",\"type\":\"BasicTicker\"},{\"attributes\":{\"label\":{\"field\":\"country\"},\"renderers\":[{\"id\":\"1477\",\"type\":\"GlyphRenderer\"}]},\"id\":\"1485\",\"type\":\"LegendItem\"},{\"attributes\":{\"grid_line_color\":null,\"ticker\":{\"id\":\"1459\",\"type\":\"BasicTicker\"}},\"id\":\"1462\",\"type\":\"Grid\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1468\",\"type\":\"HoverTool\"}]},\"id\":\"1469\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"1525\",\"type\":\"Selection\"},{\"attributes\":{\"dimension\":1,\"grid_line_color\":null,\"ticker\":{\"id\":\"1464\",\"type\":\"BasicTicker\"}},\"id\":\"1467\",\"type\":\"Grid\"},{\"attributes\":{\"axis_label\":null,\"formatter\":{\"id\":\"1483\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1464\",\"type\":\"BasicTicker\"},\"visible\":false},\"id\":\"1463\",\"type\":\"LinearAxis\"},{\"attributes\":{\"field\":\"angle\"},\"id\":\"1472\",\"type\":\"CumSum\"},{\"attributes\":{\"text\":\"Pie Chart\"},\"id\":\"1448\",\"type\":\"Title\"},{\"attributes\":{\"data_source\":{\"id\":\"1473\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1475\",\"type\":\"Wedge\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1476\",\"type\":\"Wedge\"},\"selection_glyph\":null,\"view\":{\"id\":\"1478\",\"type\":\"CDSView\"}},\"id\":\"1477\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1454\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1483\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"callback\":null,\"tooltips\":\"@country: @value\"},\"id\":\"1468\",\"type\":\"HoverTool\"},{\"attributes\":{\"field\":\"angle\",\"include_zero\":true},\"id\":\"1471\",\"type\":\"CumSum\"},{\"attributes\":{\"end_angle\":{\"expr\":{\"id\":\"1472\",\"type\":\"CumSum\"},\"units\":\"rad\"},\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"radius\":{\"units\":\"data\",\"value\":0.4},\"start_angle\":{\"expr\":{\"id\":\"1471\",\"type\":\"CumSum\"},\"units\":\"rad\"},\"x\":{\"value\":0},\"y\":{\"value\":1}},\"id\":\"1476\",\"type\":\"Wedge\"},{\"attributes\":{\"items\":[{\"id\":\"1485\",\"type\":\"LegendItem\"}]},\"id\":\"1484\",\"type\":\"Legend\"},{\"attributes\":{},\"id\":\"1526\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"axis_label\":null,\"formatter\":{\"id\":\"1481\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1459\",\"type\":\"BasicTicker\"},\"visible\":false},\"id\":\"1458\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1481\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"callback\":null,\"data\":{\"angle\":{\"__ndarray__\":\"eQLEMwAC9z+3V8R09kHrP+QcmNXVFeo/hZ74ygF34j8vDs2tzcrZP1vToA6tntg/iJh0b4xy1z93BYbhOoTUPzmtw/IJwtI/0I8to/kr0j/Qjy2j+SvSP/xUAQTZ/9A/\",\"dtype\":\"float64\",\"shape\":[12]},\"color\":[\"#3182bd\",\"#6baed6\",\"#9ecae1\",\"#c6dbef\",\"#e6550d\",\"#fd8d3c\",\"#fdae6b\",\"#fdd0a2\",\"#31a354\",\"#74c476\",\"#a1d99b\",\"#c7e9c0\"],\"country\":[\"United States\",\"United Kingdom\",\"Japan\",\"China\",\"Germany\",\"India\",\"Italy\",\"Australia\",\"Brazil\",\"France\",\"Taiwan\",\"Spain\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11],\"value\":[157,93,89,63,44,42,40,35,32,31,31,29]},\"selected\":{\"id\":\"1525\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1526\",\"type\":\"UnionRenderers\"}},\"id\":\"1473\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null},\"id\":\"1452\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1456\",\"type\":\"LinearScale\"},{\"attributes\":{\"callback\":null},\"id\":\"1450\",\"type\":\"DataRange1d\"}],\"root_ids\":[\"1447\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"33922f4a-611d-4279-8c64-4e9672655774\",\"roots\":{\"1447\":\"197247e6-bc2a-4b33-9163-2b0c00a2bca4\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1447"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from math import pi\n",
    "import pandas as pd\n",
    "from bokeh.palettes import Category20c\n",
    "from bokeh.transform import cumsum\n",
    "\n",
    "x = { 'United States': 157, 'United Kingdom': 93, 'Japan': 89, 'China': 63,\n",
    "      'Germany': 44, 'India': 42, 'Italy': 40, 'Australia': 35, 'Brazil': 32,\n",
    "      'France': 31, 'Taiwan': 31, 'Spain': 29 }\n",
    "\n",
    "data = pd.Series(x).reset_index(name='value').rename(columns={'index':'country'})\n",
    "data['color'] = Category20c[len(x)]\n",
    "\n",
    "# represent each value as an angle = value / total * 2pi\n",
    "data['angle'] = data['value']/data['value'].sum() * 2*pi\n",
    "\n",
    "p = figure(plot_height=350, title=\"Pie Chart\", toolbar_location=None,\n",
    "           tools=\"hover\", tooltips=\"@country: @value\")\n",
    "\n",
    "p.wedge(x=0, y=1, radius=0.4, \n",
    "        \n",
    "        # use cumsum to cumulatively sum the values for start and end angles\n",
    "        start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),\n",
    "        line_color=\"white\", fill_color='color', legend_field='country', source=data)\n",
    "\n",
    "p.axis.axis_label=None\n",
    "p.axis.visible=False\n",
    "p.grid.grid_line_color = None\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next transform we look at is the `linear_cmap` transform, which can generate a new sequence of colors by applying a linear colormapping to a data source column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"25cf90ac-c316-4725-892c-bb153578152c\" data-root-id=\"1569\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"036c9e92-0201-44bf-a8df-a6fe342389e7\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1578\",\"type\":\"LinearAxis\"}],\"center\":[{\"id\":\"1582\",\"type\":\"Grid\"},{\"id\":\"1587\",\"type\":\"Grid\"}],\"left\":[{\"id\":\"1583\",\"type\":\"LinearAxis\"}],\"renderers\":[{\"id\":\"1606\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"1650\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"1594\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"1570\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"1574\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"1572\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"1576\",\"type\":\"LinearScale\"}},\"id\":\"1569\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"data_source\":{\"id\":\"1602\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1604\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1605\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"1607\",\"type\":\"CDSView\"}},\"id\":\"1606\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1589\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"callback\":null},\"id\":\"1570\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1588\",\"type\":\"PanTool\"},{\"attributes\":{\"overlay\":{\"id\":\"1655\",\"type\":\"BoxAnnotation\"}},\"id\":\"1590\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"1652\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1591\",\"type\":\"SaveTool\"},{\"attributes\":{\"callback\":null},\"id\":\"1572\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1592\",\"type\":\"ResetTool\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1655\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.6},\"fill_color\":{\"field\":\"x\",\"transform\":{\"id\":\"1601\",\"type\":\"LinearColorMapper\"}},\"line_color\":{\"field\":\"x\",\"transform\":{\"id\":\"1601\",\"type\":\"LinearColorMapper\"}},\"radius\":{\"field\":\"r\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1604\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"1593\",\"type\":\"HelpTool\"},{\"attributes\":{},\"id\":\"1576\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1584\",\"type\":\"BasicTicker\"},{\"attributes\":{\"formatter\":{\"id\":\"1652\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1579\",\"type\":\"BasicTicker\"}},\"id\":\"1578\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1574\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1579\",\"type\":\"BasicTicker\"},{\"attributes\":{\"ticker\":{\"id\":\"1579\",\"type\":\"BasicTicker\"}},\"id\":\"1582\",\"type\":\"Grid\"},{\"attributes\":{\"source\":{\"id\":\"1602\",\"type\":\"ColumnDataSource\"}},\"id\":\"1607\",\"type\":\"CDSView\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"1584\",\"type\":\"BasicTicker\"}},\"id\":\"1587\",\"type\":\"Grid\"},{\"attributes\":{\"text\":\"\"},\"id\":\"1650\",\"type\":\"Title\"},{\"attributes\":{\"formatter\":{\"id\":\"1654\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1584\",\"type\":\"BasicTicker\"}},\"id\":\"1583\",\"type\":\"LinearAxis\"},{\"attributes\":{\"callback\":null,\"data\":{\"r\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[4000]},\"x\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[4000]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[4000]}},\"selected\":{\"id\":\"1656\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1657\",\"type\":\"UnionRenderers\"}},\"id\":\"1602\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1656\",\"type\":\"Selection\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"radius\":{\"field\":\"r\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1605\",\"type\":\"Circle\"},{\"attributes\":{\"high\":100,\"low\":0,\"palette\":[\"#440154\",\"#440255\",\"#440357\",\"#450558\",\"#45065A\",\"#45085B\",\"#46095C\",\"#460B5E\",\"#460C5F\",\"#460E61\",\"#470F62\",\"#471163\",\"#471265\",\"#471466\",\"#471567\",\"#471669\",\"#47186A\",\"#48196B\",\"#481A6C\",\"#481C6E\",\"#481D6F\",\"#481E70\",\"#482071\",\"#482172\",\"#482273\",\"#482374\",\"#472575\",\"#472676\",\"#472777\",\"#472878\",\"#472A79\",\"#472B7A\",\"#472C7B\",\"#462D7C\",\"#462F7C\",\"#46307D\",\"#46317E\",\"#45327F\",\"#45347F\",\"#453580\",\"#453681\",\"#443781\",\"#443982\",\"#433A83\",\"#433B83\",\"#433C84\",\"#423D84\",\"#423E85\",\"#424085\",\"#414186\",\"#414286\",\"#404387\",\"#404487\",\"#3F4587\",\"#3F4788\",\"#3E4888\",\"#3E4989\",\"#3D4A89\",\"#3D4B89\",\"#3D4C89\",\"#3C4D8A\",\"#3C4E8A\",\"#3B508A\",\"#3B518A\",\"#3A528B\",\"#3A538B\",\"#39548B\",\"#39558B\",\"#38568B\",\"#38578C\",\"#37588C\",\"#37598C\",\"#365A8C\",\"#365B8C\",\"#355C8C\",\"#355D8C\",\"#345E8D\",\"#345F8D\",\"#33608D\",\"#33618D\",\"#32628D\",\"#32638D\",\"#31648D\",\"#31658D\",\"#31668D\",\"#30678D\",\"#30688D\",\"#2F698D\",\"#2F6A8D\",\"#2E6B8E\",\"#2E6C8E\",\"#2E6D8E\",\"#2D6E8E\",\"#2D6F8E\",\"#2C708E\",\"#2C718E\",\"#2C728E\",\"#2B738E\",\"#2B748E\",\"#2A758E\",\"#2A768E\",\"#2A778E\",\"#29788E\",\"#29798E\",\"#287A8E\",\"#287A8E\",\"#287B8E\",\"#277C8E\",\"#277D8E\",\"#277E8E\",\"#267F8E\",\"#26808E\",\"#26818E\",\"#25828E\",\"#25838D\",\"#24848D\",\"#24858D\",\"#24868D\",\"#23878D\",\"#23888D\",\"#23898D\",\"#22898D\",\"#228A8D\",\"#228B8D\",\"#218C8D\",\"#218D8C\",\"#218E8C\",\"#208F8C\",\"#20908C\",\"#20918C\",\"#1F928C\",\"#1F938B\",\"#1F948B\",\"#1F958B\",\"#1F968B\",\"#1E978A\",\"#1E988A\",\"#1E998A\",\"#1E998A\",\"#1E9A89\",\"#1E9B89\",\"#1E9C89\",\"#1E9D88\",\"#1E9E88\",\"#1E9F88\",\"#1EA087\",\"#1FA187\",\"#1FA286\",\"#1FA386\",\"#20A485\",\"#20A585\",\"#21A685\",\"#21A784\",\"#22A784\",\"#23A883\",\"#23A982\",\"#24AA82\",\"#25AB81\",\"#26AC81\",\"#27AD80\",\"#28AE7F\",\"#29AF7F\",\"#2AB07E\",\"#2BB17D\",\"#2CB17D\",\"#2EB27C\",\"#2FB37B\",\"#30B47A\",\"#32B57A\",\"#33B679\",\"#35B778\",\"#36B877\",\"#38B976\",\"#39B976\",\"#3BBA75\",\"#3DBB74\",\"#3EBC73\",\"#40BD72\",\"#42BE71\",\"#44BE70\",\"#45BF6F\",\"#47C06E\",\"#49C16D\",\"#4BC26C\",\"#4DC26B\",\"#4FC369\",\"#51C468\",\"#53C567\",\"#55C666\",\"#57C665\",\"#59C764\",\"#5BC862\",\"#5EC961\",\"#60C960\",\"#62CA5F\",\"#64CB5D\",\"#67CC5C\",\"#69CC5B\",\"#6BCD59\",\"#6DCE58\",\"#70CE56\",\"#72CF55\",\"#74D054\",\"#77D052\",\"#79D151\",\"#7CD24F\",\"#7ED24E\",\"#81D34C\",\"#83D34B\",\"#86D449\",\"#88D547\",\"#8BD546\",\"#8DD644\",\"#90D643\",\"#92D741\",\"#95D73F\",\"#97D83E\",\"#9AD83C\",\"#9DD93A\",\"#9FD938\",\"#A2DA37\",\"#A5DA35\",\"#A7DB33\",\"#AADB32\",\"#ADDC30\",\"#AFDC2E\",\"#B2DD2C\",\"#B5DD2B\",\"#B7DD29\",\"#BADE27\",\"#BDDE26\",\"#BFDF24\",\"#C2DF22\",\"#C5DF21\",\"#C7E01F\",\"#CAE01E\",\"#CDE01D\",\"#CFE11C\",\"#D2E11B\",\"#D4E11A\",\"#D7E219\",\"#DAE218\",\"#DCE218\",\"#DFE318\",\"#E1E318\",\"#E4E318\",\"#E7E419\",\"#E9E419\",\"#ECE41A\",\"#EEE51B\",\"#F1E51C\",\"#F3E51E\",\"#F6E61F\",\"#F8E621\",\"#FAE622\",\"#FDE724\"]},\"id\":\"1601\",\"type\":\"LinearColorMapper\"},{\"attributes\":{},\"id\":\"1657\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"1654\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1588\",\"type\":\"PanTool\"},{\"id\":\"1589\",\"type\":\"WheelZoomTool\"},{\"id\":\"1590\",\"type\":\"BoxZoomTool\"},{\"id\":\"1591\",\"type\":\"SaveTool\"},{\"id\":\"1592\",\"type\":\"ResetTool\"},{\"id\":\"1593\",\"type\":\"HelpTool\"}]},\"id\":\"1594\",\"type\":\"Toolbar\"}],\"root_ids\":[\"1569\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"036c9e92-0201-44bf-a8df-a6fe342389e7\",\"roots\":{\"1569\":\"25cf90ac-c316-4725-892c-bb153578152c\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1569"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.transform import linear_cmap\n",
    "\n",
    "N = 4000\n",
    "data = dict(x=np.random.random(size=N) * 100,\n",
    "            y=np.random.random(size=N) * 100,\n",
    "            r=np.random.random(size=N) * 1.5)\n",
    "\n",
    "p = figure()\n",
    "\n",
    "p.circle('x', 'y', radius='r', source=data, fill_alpha=0.6,\n",
    "        \n",
    "         # color map based on the x-coordinate\n",
    "         color=linear_cmap('x', 'Viridis256', 0, 100))\n",
    "\n",
    "show(p) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the code above to use `log_cmap` and observe the results. Try changing `low` and `high` and specificying `low_color` and `high_color`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exercise: use the corresponding factor_cmap to color map a scatter plot of the iris data set\n",
    "\n",
    "from bokeh.sampledata.iris import flowers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
